Due to the high sensitivity of the human auditory perception system, audio quality is an important marketing parameter for equipment producing audio. In modern systems, a lot of audio post-processing is done to alter an actual signal which is sent to the speakers. Because of the need for high audio quality, a lot of tuning is needed inside these systems to integrate all features, while preserving a high quality of the output signal. This tuning is usually done after all features have been integrated in the system. Mostly, this tuning is based on avoidance of any overflow in the signal. To achieve this, usually the signal is scaled down at an input of the system to create so-called headroom for further features to be realized. This headroom is then filled by some or all of the features implemented in the device. However, because of the scaling, signal precision is lost, leading to increased quantification noise in the output signal of digital audio processing systems. Furthermore, in the known devices, the required tuning task has to be performed manually and requires a high level of audio expertise. As a result, the required tuning is rather expensive and time-consuming.
For example, US 2002/0023120 A1 relates to a method for digitally processing multimedia data including an audio signal.
In devices digitally post-processing audio data before the audio data is output to a speaker, a plurality of features for altering the sound is typically provided. These features may include volume control, tone control, equalization, compression/expansion, voice filtering, limiter processing, etc. realized by amplification, attenuation, low-pass filtering, high-pass filtering, band pass filtering, band-stop filtering, etc. and forming a large number of processing tasks which have to be realized by algorithms in a digital signal processing unit. The respective algorithms are performed on the input audio signal one after the other in a sequence.
To keep costs, required memory and/or area needed for digital audio signal processing low, in many cases, instead of floating point processing providing higher accuracy, processing is done by fixed point processing. However, such fixed point processing leads to a limited signal to noise ratio and requires considerable headroom.
It has been found that the achieved result for the quality of the output audio signal (after the processing tasks have been applied to the input signal) strongly depends on the sequence in which the different tasks are performed. In many cases, the sequence (order of processing tasks) with which the best quality of the output audio signal is achieved differs from the expected one. Thus, the results for an optimum sequence of processing tasks are often counter-intuitive and may even change if an additional processing task is introduced or the signal characteristics of the input audio signal change.
Since usually a large number n of possible processing tasks is implemented, the best sequence of processing tasks for achieving the highest possible output audio signal quality cannot easily be found by manually trying different sequences of the processing tasks. In this context it should be noted that the number of possible sequences (=number of possible permutations in the order of the desired processing tasks) is n!.